{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f0e0461c",
   "metadata": {},
   "source": [
    "# 题目"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9825af9",
   "metadata": {},
   "source": [
    "* 1.题目一:请筛选出拥有地铁站点最多的前15的城市制作成柱状图\n",
    "* 2.题目二:请筛选出拥有>地铁线路最多的前15的城市制作成柱状图\n",
    "* 3,题目三:统计广州换乘次数为1次、2次、大于等于3次的站点人数(例如广州的嘉天望岗可换乘2号线、3号线、14号线，则计为可换乘次数为2次)，并以饼状图的形式展示换乘站点的比例\n",
    "* 4.题目四: 统计同一城市中换乘站点最多的前15的城市，并以折线图展示\n",
    "* 5.题目五:统计广州地铁线路各线路站点的数量，并以横向柱转图展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cb2a98c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "df= pd.read_csv('metro.csv',encoding='gbk',sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4405f050",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>站点名称</th>\n",
       "      <th>POI编号</th>\n",
       "      <th>拼音名称</th>\n",
       "      <th>gd经度</th>\n",
       "      <th>gd纬度</th>\n",
       "      <th>路线名称</th>\n",
       "      <th>城市名称</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>金安桥</td>\n",
       "      <td>BV11494889</td>\n",
       "      <td>JinAn Qiao</td>\n",
       "      <td>116.162586</td>\n",
       "      <td>39.923298</td>\n",
       "      <td>S1线</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>四道桥</td>\n",
       "      <td>BV10813547</td>\n",
       "      <td>si dao qiao</td>\n",
       "      <td>116.134010</td>\n",
       "      <td>39.916030</td>\n",
       "      <td>S1线</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>桥户营</td>\n",
       "      <td>BV10813541</td>\n",
       "      <td>qiao hu ying</td>\n",
       "      <td>116.125809</td>\n",
       "      <td>39.912383</td>\n",
       "      <td>S1线</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>上岸</td>\n",
       "      <td>BV10813543</td>\n",
       "      <td>shang an</td>\n",
       "      <td>116.122225</td>\n",
       "      <td>39.905138</td>\n",
       "      <td>S1线</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>栗园庄</td>\n",
       "      <td>BV10813544</td>\n",
       "      <td>li yuan zhuang</td>\n",
       "      <td>116.123254</td>\n",
       "      <td>39.895780</td>\n",
       "      <td>S1线</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5029</th>\n",
       "      <td>锡林公园</td>\n",
       "      <td>BV10875474</td>\n",
       "      <td>XiLin GongYuan</td>\n",
       "      <td>111.687460</td>\n",
       "      <td>40.770883</td>\n",
       "      <td>2号线</td>\n",
       "      <td>呼和浩特</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5030</th>\n",
       "      <td>内大南校区</td>\n",
       "      <td>BV10875440</td>\n",
       "      <td>NeiDaNan XiaoQu</td>\n",
       "      <td>111.692463</td>\n",
       "      <td>40.759146</td>\n",
       "      <td>2号线</td>\n",
       "      <td>呼和浩特</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5031</th>\n",
       "      <td>帅家营</td>\n",
       "      <td>BV10875453</td>\n",
       "      <td>ShuaiJiaYing</td>\n",
       "      <td>111.706981</td>\n",
       "      <td>40.755604</td>\n",
       "      <td>2号线</td>\n",
       "      <td>呼和浩特</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5032</th>\n",
       "      <td>喇嘛营</td>\n",
       "      <td>BV10875441</td>\n",
       "      <td>LaMaYing</td>\n",
       "      <td>111.731336</td>\n",
       "      <td>40.755684</td>\n",
       "      <td>2号线</td>\n",
       "      <td>呼和浩特</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5033</th>\n",
       "      <td>阿尔山路</td>\n",
       "      <td>BV10875438</td>\n",
       "      <td>AErShan Lu</td>\n",
       "      <td>111.747816</td>\n",
       "      <td>40.755465</td>\n",
       "      <td>2号线</td>\n",
       "      <td>呼和浩特</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5034 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       站点名称       POI编号             拼音名称        gd经度       gd纬度 路线名称  城市名称\n",
       "0       金安桥  BV11494889       JinAn Qiao  116.162586  39.923298  S1线    北京\n",
       "1       四道桥  BV10813547      si dao qiao  116.134010  39.916030  S1线    北京\n",
       "2       桥户营  BV10813541     qiao hu ying  116.125809  39.912383  S1线    北京\n",
       "3        上岸  BV10813543         shang an  116.122225  39.905138  S1线    北京\n",
       "4       栗园庄  BV10813544   li yuan zhuang  116.123254  39.895780  S1线    北京\n",
       "...     ...         ...              ...         ...        ...  ...   ...\n",
       "5029   锡林公园  BV10875474   XiLin GongYuan  111.687460  40.770883  2号线  呼和浩特\n",
       "5030  内大南校区  BV10875440  NeiDaNan XiaoQu  111.692463  40.759146  2号线  呼和浩特\n",
       "5031    帅家营  BV10875453     ShuaiJiaYing  111.706981  40.755604  2号线  呼和浩特\n",
       "5032    喇嘛营  BV10875441         LaMaYing  111.731336  40.755684  2号线  呼和浩特\n",
       "5033   阿尔山路  BV10875438       AErShan Lu  111.747816  40.755465  2号线  呼和浩特\n",
       "\n",
       "[5034 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acf7994a",
   "metadata": {},
   "source": [
    "# 1.题目一:请筛选出拥有地铁站点最多的前15的城市制作成柱状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e1f7cc99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>上海</td>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>北京</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>成都</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>广州</td>\n",
       "      <td>284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>深圳</td>\n",
       "      <td>284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>武汉</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>杭州</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>重庆</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>南京</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>西安</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>天津</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>郑州</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>苏州</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>香港</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>长沙</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   city  num\n",
       "0    上海  535\n",
       "1    北京  413\n",
       "2    成都  356\n",
       "3    广州  284\n",
       "4    深圳  284\n",
       "5    武汉  243\n",
       "6    杭州  225\n",
       "7    重庆  218\n",
       "8    南京  174\n",
       "9    西安  166\n",
       "10   天津  159\n",
       "11   郑州  146\n",
       "12   苏州  135\n",
       "13   香港  131\n",
       "14   长沙  114"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_city_metro_num = df['城市名称'].value_counts()\n",
    "dict_city_metro_num = {'city':df_city_metro_num.index[0:15],'num': df_city_metro_num.values[0:15]}\n",
    "df_city_15=pd.DataFrame(dict_city_metro_num)\n",
    "df_city_15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f4cc1e90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画图 可参考链接https://zhuanlan. zhihu com p/438794094\n",
    "fig = plt.figure()\n",
    "plt.figure(figsize=(20,8),dpi=100)\n",
    "x_label = df_city_metro_num.index[0:15]\n",
    "y_label = df_city_metro_num.values[0:15]\n",
    "plt.bar(x_label,y_label)\n",
    "for xy in zip(x_label,y_label):\n",
    "    plt.text(x,y,'%.2f'%y,ha='center',va='bottom')\n",
    "plt.rcParams['font.sans-serif']=['simhei']\n",
    "plt.rcParams['axes.unicode_minus' ]=False\n",
    "plt.title(\"地铁站点最多的前15的城市\")\n",
    "plt.ylabel(\"数量\")\n",
    "plt.xlabel(\"城市\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3897e109",
   "metadata": {},
   "source": [
    "# 2.题目二:请筛选出拥有>地铁线路最多的前15的城市制作成柱状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3fd7b520",
   "metadata": {},
   "outputs": [],
   "source": [
    "city_Line=df.groupby(\"城市名称\")[\"路线名称\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "15c3723e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('北京', 24),\n",
       " ('上海', 19),\n",
       " ('广州', 16),\n",
       " ('成都', 12),\n",
       " ('香港', 11),\n",
       " ('南京', 10),\n",
       " ('武汉', 10),\n",
       " ('深圳', 10),\n",
       " ('重庆', 10),\n",
       " ('西安', 8),\n",
       " ('大连', 7),\n",
       " ('杭州', 7),\n",
       " ('郑州', 7),\n",
       " ('天津', 6),\n",
       " ('昆明', 6),\n",
       " ('长沙', 6),\n",
       " ('青岛', 6),\n",
       " ('苏州', 5),\n",
       " ('长春', 5),\n",
       " ('南宁', 4),\n",
       " ('合肥', 4),\n",
       " ('宁波', 4),\n",
       " ('沈阳', 4),\n",
       " ('南昌', 3),\n",
       " ('无锡', 3),\n",
       " ('济南', 3),\n",
       " ('石家庄', 3),\n",
       " ('厦门', 2),\n",
       " ('呼和浩特', 2),\n",
       " ('哈尔滨', 2),\n",
       " ('徐州', 2),\n",
       " ('福州', 2),\n",
       " ('贵阳', 2),\n",
       " ('东莞', 1),\n",
       " ('乌鲁木齐', 1),\n",
       " ('佛山', 1),\n",
       " ('兰州', 1),\n",
       " ('太原', 1),\n",
       " ('常州', 1),\n",
       " ('温州', 1)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_city_line={}\n",
    "for i in range(len(city_Line)):\n",
    "    dict_city_line[city_Line.index[i][0]]=dict_city_line.get(city_Line.index[i][0],0) + 1\n",
    "dict_sort=sorted(dict_city_line.items(),key=lambda x:x[1],reverse=True)\n",
    "dict_sort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "87d8ac11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['北京',\n",
       " '上海',\n",
       " '广州',\n",
       " '成都',\n",
       " '香港',\n",
       " '南京',\n",
       " '武汉',\n",
       " '深圳',\n",
       " '重庆',\n",
       " '西安',\n",
       " '大连',\n",
       " '杭州',\n",
       " '郑州',\n",
       " '天津',\n",
       " '昆明']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities=[dict_sort[i][0] for i in range(15)]\n",
    "num=[dict_sort[i][1] for i in range(15)]\n",
    "cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "08b17642",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.figure(figsize=(20,8),dpi=100)\n",
    "x_label = cities\n",
    "y_label = num\n",
    "plt.bar(x_label,y_label,color='green')\n",
    "for x,y in zip(x_label,y_label):\n",
    "    plt.text(x,y,'%.2f'%y,ha='center',va='bottom')\n",
    "plt.rcParams['font.sans-serif']=['simhei']\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "plt.title(\"地铁线路最多的前15的城市\")\n",
    "plt.ylabel(\"数量\")\n",
    "plt.xlabel(\"城市\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61c3a953",
   "metadata": {},
   "source": [
    "# 3.题目三:统计广州换乘次数为1次、2次、大于等于3次的站点人数(例如广州的嘉天望岗可换乘2号线、3号线、14号线，则计为可换乘次数为2次)，并以饼状图的形式展示换乘站点的比例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2dc173f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "city_station=df.query('城市名称==\"广州\"')['站点名称'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ec842683",
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_city_station ={'city':[i[0]for i in city_station.index],'station':[i[1] for i in city_station.index],'num':city_station.values}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c286c0f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_city_station=pd.DataFrame(dict_city_station)\n",
    "station_count1=len(df_city_station.query('num==1'))\n",
    "station_count2=len(df_city_station.query('num==2'))\n",
    "station_count_more3=len(df_city_station.query('num==3'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "72265aed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.figure(figsize=(10,10),dpi=100)\n",
    "x_label = [station_count1,station_count2,station_count_more3]\n",
    "y_label = ['单个站点换乘次数为1','单个站点换乘次数为2','单个站点换乘次数为大于或等于3']\n",
    "plt.pie(x_label,labels=y_label,autopct='%1.1f%%')\n",
    "plt.rcParams['font.sans-serif']=['simhei']\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "plt.title(\"广州市单站点换乘次数比例分布\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4216ee9f",
   "metadata": {},
   "source": [
    "# 4.题目四: 统计同一城市中换乘站点最多的前15的城市，并以折线图展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "aeb5243b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>上海</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>北京</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>成都</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>杭州</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>深圳</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>广州</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>香港</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>武汉</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>重庆</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>昆明</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>郑州</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>大连</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>天津</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>南京</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>西安</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   city  num\n",
       "0    上海  114\n",
       "1    北京   63\n",
       "2    成都   59\n",
       "3    杭州   43\n",
       "4    深圳   41\n",
       "5    广州   37\n",
       "6    香港   29\n",
       "7    武汉   28\n",
       "8    重庆   25\n",
       "9    昆明   21\n",
       "10   郑州   17\n",
       "11   大连   17\n",
       "12   天津   15\n",
       "13   南京   13\n",
       "14   西安   13"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tran_station_city=df_city_station.query('num>=2')['city'].value_counts()\n",
    "dict_tran_station_city = {'city':df_tran_station_city.index[0:15],'num':df_tran_station_city.values[0:15]}\n",
    "df_tran_station_city_15=pd.DataFrame(dict_tran_station_city)\n",
    "df_tran_station_city_15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cac2183a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.figure(figsize=(10,10),dpi=100)\n",
    "x_label = df_tran_station_city.index[0:15]\n",
    "y_label = df_tran_station_city.values[0:15]\n",
    "plt.plot(x_label,y_label,color = 'r',linewidth=1.0,linestyle='--')\n",
    "plt.scatter(x_label,y_label)\n",
    "for i in range(len(x_label)):\n",
    "    plt.text(x_label[i],y_label[i],(x_label[i],y_label[i]))\n",
    "plt.title(\"可换乘站点数量最多的前15的城市\")\n",
    "plt.ylabel(\"可换乘站点的数量\")\n",
    "plt.xlabel(\"城市\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ba40179",
   "metadata": {},
   "source": [
    "# 5.题目五:统计广州地铁线路各线路站点的数量，并以横向柱转图展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bf17f1d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_广州=df.query('城市名称==\"广州\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "68fc981d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6号线             31\n",
       "8号线             26\n",
       "广佛线             25\n",
       "2号线             24\n",
       "5号线             24\n",
       "4号线             23\n",
       "21号线            21\n",
       "1号线             16\n",
       "3号线             16\n",
       "3号线(北延段)        15\n",
       "14号线            13\n",
       "9号线             11\n",
       "13号线            11\n",
       "14号线支线(知识城线)    10\n",
       "7号线              9\n",
       "APM线             9\n",
       "Name: 路线名称, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_广州['路线名称'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "eeeb9643",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name=df_广州['路线名称'].value_counts().index\n",
    "colleges=df_广州['路线名称'].value_counts().values\n",
    "plt.figure(figsize = (15,8),dpi = 60)\n",
    "b=plt.barh(name,colleges,color='red')\n",
    "for rect in b:\n",
    "    w=rect.get_width()\n",
    "    plt.text(w,rect.get_y()+rect.get_height()/2,'%d'%int(w),ha='left',va='center')\n",
    "plt.title(\"广州各地铁线路站点数量\",fontsize=18)\n",
    "plt.xlabel(\"路线名称\",fontsize=15)\n",
    "plt.ylabel('数量',fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8440d1a6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
